In the era of the 4th industrial revolution of big data, artificial intelligence (AI) is widely used in each and every field of composite materials which includes design and analysis, material storage, manufacturing, non-destructive testing, structural health monitoring (SHM) and prognostics of its remaining useful life, material state (MS) and damage modes. While these AI models are rapidly developed and integrated into the industrial internet of things to keep track of the health of a composite material from its birth to death, these integrations remain uncertain for prognostics without the certainty of its previous MS. This article is a comprehensive review of the AI models being developed over the past few decades in the field of SHM and prognostics health management of polymer matrix composites. It further analyzes the real gaps between these developments and the nature of uncertainty of these methods. Finally, the pipeline for the real-time prognostics from birth to death, hybrid approaches, uncertainty quantification of data-driven and physics-based systems, and its reliability standards to such complex advanced composite materials are discussed. This paper will be focused as a basic guide for researchers implementing AI in composites for diagnosis, prognosis, and control.

The principal objective of this study is to employ non-destructive broadband dielectric spectroscopy/impedance spectroscopy and machine learning techniques to estimate the moisture content in FRP composites under hygrothermal aging. Here, classification and regression machine learning models that can accurately predict the current moisture saturation state are developed using the frequency domain dielectric response of the composite, in conjunction with the time domain hygrothermal aging effect. First, to categorize the composites based on the present state of the absorbed moisture supervised classification learning models (i.e., quadratic discriminant analysis (QDA), support vector machine (SVM), and artificial neural network-based multilayer perceptron (MLP) classifier) have been developed. Later, to accurately estimate the relative moisture absorption from the dielectric data, supervised regression models (i.e., multiple linear regression (MLR), decision tree regression (DTR), and multi-layer perceptron (MLP) regression) have been developed, which can effectively estimate the relative moisture absorption from the dielectric response of the material with an R¬2 value greater than 0.95. The physics behind the hygrothermal aging of the composites has then been interpreted by comparing the model attributes to see which characteristics most strongly influence the predictions.

The use of fiber-reinforced composite materials has widely spread in various sectors, including aerospace, defense, and civil industry. The assessment of these heterogeneous material systems is important for safer and risk-free applications and has contributed to the need for self-sensing composites. This work is focused on the development of piezoresistive composites, the prediction of their performance and structural health monitoring (SHM). Additionally, this work unpacks the complexity of carbon nanotubes (CNTs) micro-fabrication and the development of piezoresistive and electromagnetic (EM) waves detection electrodes. Scanning electron microscopy (SEM) was used to characterize the CNTs structure and morphologies. The manufactured CNTs were incorporated in epoxy systems to fabricate glass fiber reinforced polymer (GFRP)-CNTs smart composites with piezoresistive properties. The detection of micro-damage onset and its progression was carried out in mode I, to evaluate the sensitivity of the smart composites to damage development. The change in electrical conductivity of the nanotubes-reinforced composite systems due to localized mechanical strains enabled crack propagation detection. The relationship between crack propagation, fracture toughness, and electrical resistivity of the smart composite was analyzed.